Analysis of chemical exposures in racial populations in Canada: An investigation based on the Canadian health measures survey
Bibliographic record
Abstract
Despite demonstrated disparities in environmental chemical exposures by racial identity, no Canadian study has systematically assessed the feasibility of using a nationally representative dataset to examine differences in chemical concentrations by race. We assessed the feasibility and constraints of analysing chemical exposures in racial populations, including visible minorities and populations of Indigenous identity, using biomonitoring data collected through the Canadian Health Measures Survey (CHMS). Our primary objectives were to assess the ability to 1) generate geometric means and percentiles of chemical concentrations for racial populations by age or sex, 2) statistically compare concentrations among racial populations, and 3) calculate time trends of concentrations by race. We conducted these analyses for several priority chemicals: lead, cadmium, benzene, bisphenol A (BPA), and di(2-ethylhexyl) phthalate (DEHP). Survey participants self-identified as one of the following: White, Black, East and Southeast Asian, South Asian, Middle Eastern, Latin American, First Nations, Metis, and Inuit. Analyses were conducted for individual and combined cycles of the CHMS. Using data from the latest CHMS cycle in which each chemical was measured, we observed that sample sizes were sufficient to report geometric mean concentrations for all races except Inuit. Due to privacy considerations associated with small sample sizes, the 5th and 95th percentile concentrations could not be consistently reported for all racial populations in this analysis. While we were able to statistically compare concentrations among racial populations, the analysis was constrained by the limited number of statistical degrees of freedom available in a single CHMS cycle. Both of these constraints were alleviated by combining multiple cycles of data. The analysis of time trends was less subject to privacy and statistical limitations; we were able to calculate time trends of chemical concentrations for all racial populations. Our findings provide an important baseline for follow-up investigations of descriptive and etiological analyses of environmental chemical exposures and race in the CHMS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".